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April 13, 2026IEEE Transactions on Pattern Analysis and Machine Intelligence

SCGT: Towards Scalable and Comprehensive Graph Transformer

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Authors

JLJianqing LiangMCMin ChenXWXinkai Wei

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Overview

This work demonstrates improved graph representation learning performance in various datasets, implying enhanced efficiency and expressiveness.

Key Points

  • The aim is to develop a scalable and powerful Graph Transformer architecture that captures complex graph structures.
  • Developed a Scalable and Comprehensive Graph Transformer (SCGT) architecture.
  • Utilized Focused Graph Linear Attention (FGLA) for sharper attention score distributions.
  • Implemented Comprehensive Positional Encoding (CPE) to enhance node-level feature awareness.
  • Conducted extensive empirical analysis on 12 datasets.
  • SCGT achieved competitive performance compared to existing methods.
  • Demonstrated efficiency while maintaining expressiveness across diverse datasets.
  • Theoretical analysis supports improved performance due to enhanced inductive bias.

Cite This Study

Liang et al. (2026) studied this question.

synapsesocial.com/papers/69dc87983afacbeac03e9d72https://doi.org/10.1109/tpami.2026.3682858
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  1. 1AnchorGT: Efficient and Flexible Attention Architecture for Scalable Graph Transformers2024
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